Network AI MCP Server

1

Add it to Claude Code

Run this in a terminal.

Run in terminal
claude mcp add network-ai -- npx -y network-ai-server
README.md

Multi-agent swarm orchestrator for AI workflows

Network-AI

TypeScript/Node.js multi-agent orchestrator — shared state, guardrails, budgets, and cross-framework coordination

Network-AI is a TypeScript/Node.js multi-agent orchestrator that adds coordination, guardrails, and governance to any AI agent stack.

  • Shared blackboard with locking — atomic propose → validate → commit prevents race conditions and split-brain failures across parallel agents
  • Guardrails and budgets — FSM governance, per-agent token ceilings, HMAC / Ed25519 audit trails, and permission gating
  • 17 adapters — LangChain (+ streaming), AutoGen, CrewAI, OpenAI Assistants, LlamaIndex, Semantic Kernel, Haystack, DSPy, Agno, MCP, Custom (+ streaming), OpenClaw, A2A, Codex, MiniMax, NemoClaw, and APS — no glue code, no lock-in
  • Persistent project memory (Layer 3)context_manager.py injects decisions, goals, stack, milestones, and banned patterns into every system prompt so agents always have full project context

The silent failure mode in multi-agent systems: parallel agents writing to the same key use last-write-wins by default — one agent's result silently overwrites another's mid-flight. The outcome is split-brain state: double-spends, contradictory decisions, corrupted context, no error thrown. Network-AI's propose → validate → commit mutex prevents this at the coordination layer, before any write reaches shared state.

Use Network-AI as:

  • A TypeScript/Node.js libraryimport { createSwarmOrchestrator } from 'network-ai'
  • An MCP servernpx network-ai-server --port 3001
  • A CLInetwork-ai bb get status / network-ai audit tail
  • An OpenClaw skillclawhub install network-ai

**5-minute quickstart →**  |  **Architecture →**  |  **All adapters →**  |  **Benchmarks →**


⚡ Try in 60 Seconds

npm install network-ai
import { LockedBlackboard } from 'network-ai';

const board = new LockedBlackboard('.');
const id    = board.propose('status', { ready: true }, 'agent-1');
board.validate(id, 'agent-1');
board.commit(id);

console.log(board.read('status'));  // { ready: true }

Two agents, atomic writes, no race conditions. That's it.

Want the full stress test? No API key, ~3 seconds:

npx ts-node examples/08-control-plane-stress-demo.ts

Runs priority preemption, AuthGuardian permission gating, FSM governance, and compliance monitoring — all without a single LLM call.

If it saves you from a race condition, a ⭐ helps others find it.


What's Included

✅ Atomic shared state propose → validate → commit with filesystem mutex — no split-brain
✅ Token budgets Hard per-agent ceilings with live spend tracking
✅ Permission gating HMAC / Ed25519-signed tokens, scoped per agent and resour

Tools (3)

blackboard_readRead data from the shared blackboard
blackboard_writePropose and commit data to the shared blackboard
audit_logRetrieve audit logs for agent activities

Environment Variables

NETWORK_AI_PORTPort for the MCP server to listen on

Configuration

claude_desktop_config.json
{"mcpServers": {"network-ai": {"command": "npx", "args": ["-y", "network-ai-server"]}}}

Try it

Read the current status from the shared blackboard.
Propose a new state update for the 'project-milestone' key.
Retrieve the last 10 audit logs to verify agent activity.
Check the current token budget usage for the active agents.

Frequently Asked Questions

What are the key features of Network AI?

Atomic shared blackboard with locking to prevent race conditions. FSM governance and per-agent token budget enforcement. Permission gating using HMAC/Ed25519 audit trails. Support for 17 AI framework adapters including LangChain and CrewAI. Persistent project memory injection into system prompts.

What can I use Network AI for?

Coordinating parallel agents to prevent split-brain state in complex workflows. Enforcing strict token usage limits across multiple AI agents. Maintaining a secure audit trail of agent decisions for compliance. Managing shared project context across heterogeneous AI frameworks.

How do I install Network AI?

Install Network AI by running: npx network-ai-server --port 3001

What MCP clients work with Network AI?

Network AI works with any MCP-compatible client including Claude Desktop, Claude Code, Cursor, and other editors with MCP support.

Turn this server into reusable context

Keep Network AI docs, env vars, and workflow notes in Conare so your agent carries them across sessions.

Need the old visual installer? Open Conare IDE.
Open Conare